October 14, 2025 • 48 mins
When AI enters the enterprise, the work isn’t just about the tech—it’s about culture, collaboration, and courage. In this episode, Galen chats with Deborah Ketai, a program and change management leader who helped a Fortune 5 healthcare organization align its people, systems, and culture around AI. Together, they unpack how she built a community of practice that broke down silos, reduced knowledge debt, and created space for cross-training, collaboration, and smarter risk management.
From talent strategy to trust and transparency, Deborah shares what it really takes to sustain AI-driven change inside complex organizations—and what PMs need to learn now to stay ahead as their roles evolve.
Resources from this episode:
- Join DPM Membership
- Subscribe to the newsletter to get our latest articles and podcasts
- Connect with Deborah on LinkedIn
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Galen Low (00:00):
Is it worth the effort to try to
break down silos and alignattitudes around AI within
a massive organization?
Is it even possible?
Deborah Ketai (00:08):
If they skip the step, they're going to
incur a lot of debt, technicaldebt, knowledge debt that is
gonna make future projects moreexpensive and more difficult?
Galen Low (00:20):
You had called out that it wasn't just generative
AI, in fact, this is morelike foundational change.
Deborah Ketai (00:25):
We had really three goals — talent, mobility,
and retention to encouragecollaboration between these
AI teams so that they'renot reinventing the wheel.
And the third is a culturalshift, encouraging business
champions to considerAI solutions to their
business challenges.
Galen Low (00:44):
Some folks who are being asked to do a dual role.
Do all projects needchange management?
Deborah Ketai (00:49):
Not all projects require change management.
The skillsets aredifferent but overlapping.
Program managers need toget closer to where strategy
is formed to be transparentin reflecting back to
leadership where they'relikely to be misalignments.
Galen Low (01:15):
Welcome to The Digital Project Manager
podcast — the show thathelps delivery leaders work
smarter, deliver faster, andlead better in the age of AI.
I'm Galen, and every week wedive into real world strategies,
new tools, proven frameworks,and the occasional war story
from the project front lines.
Whether you're steering massivetransformation projects,
wrangling AI workflows,we're just trying to keep
(01:36):
the chaos under control.
You're in the right place.
Let's get into it.
Okay, today we're peeringbehind the curtain of how a
program manager and her teamcreated a community of practice
within a massive healthcareenterprise that helped remove
silos and align attitudesaround AI within the business.
My guest today is that veryprogram manager, Deborah Ketai.
(01:58):
Deborah is a certifiedorganizational change
manager and program managermost recently for a massive
US-based healthcare group,where she built thriving
communities around AI, DEI,and data transmission controls.
Deborah has also been veryactive in building the
project management communityas a board member and
programs portfolio lead fora regional chapter of PMI.
(02:19):
In this episode, we're gonnacover what Deborah's day-to-day
looked like as a program managerleading a major organizational
change initiative around AI,whether AI and machine learning
related organizational changeneeds to be treated differently,
what teams of any size canborrow from the playbooks
of enterprises undergoingmassive AI transformations,
and whether the role of aproject or program manager will
(02:42):
evolve to incorporate changemanagement and what that might
mean for the PM skillset.
Deborah, thank you somuch for being here today.
Deborah Ketai (02:49):
Thank you for the opportunity, Galen.
Galen Low (02:51):
I'm so excited about this conversation because you
are just someone who has beenlike very deeply involved and
embedded as a program lead andchange manager for a massive
healthcare enterprise just as AIwas beginning to enter the fray.
In our conversations leading upto this, you've mentioned things
like breaking down silos betweendivisions and stakeholder
groups, running trainingsessions and hackathons,
(03:14):
addressing fears around riskand compliance, and also just
getting AI practitioners andmaybe the AI practitioner
wannabes all on the same page.
So I thought maybe I'djust start with like one
hot question that I thinkmy listeners want to know.
My question is this.
Is it worth the effort andenergy to try to break down
silos and align attitudesaround AI within a massive
(03:34):
enterprise organization?
Is it even possible?
And what would happen ifan organization like the
one you were working withskip that step completely?
Deborah Ketai (03:42):
That's really a good question, Galen,
and I'm glad you asked it.
I think alignment is going to becritical and breaking down silos
is gonna be a big piece of that.
First of all, companies aregoing to need to establish
data and AI governance.
They're going to needto align their resources
and expectations arounddeveloping or buying AI tools.
(04:06):
And if they want to have an AIfirst culture, and they're going
to have to, if their competitorsare doing it, they're going to
need to think about what thatmeans for their employees,
both now and in the future.
So at a bare minimum, they'regoing to need to break down
silos to avoid the risk ofcybersecurity incidents.
(04:27):
If they skip the stepof breaking down silos,
they're also going toincur a lot of what we
call debt, technical debt,knowledge, debt, and so on.
That is gonna make futureprojects more expensive
and more difficult.
I think there are three areasin which I've broken down silos.
(04:49):
You really mentionedall of them.
AI, system operating controls,and as community engagement,
lead of the second largestemployee resource group
at United Health Group.
And also to some extentthrough a mentorship program,
breaking down silos aftermergers and acquisitions.
So I think that there area number of different steps
(05:12):
involved and skills involvedin breaking down silos that
project managers and changemanagers need to focus on.
One is creating empathyamong different groups
by familiarizingthem with each other.
Cross-training, job shadowing,mentoring and coaching.
Leadership needs to create andget buy-in on the vision of
(05:34):
what it would look like andwhat the benefits would be
of breaking down the silos.
They need to create andconcentrate on touch
points, processes, andcommunication channels that
bring the silos together.
And they also need toconcentrate on eliminating
obstacles to bringing peopletogether such as different data
sources is one of the big ones.
Galen Low (05:56):
What I really like about your answer there is,
I was kind of thinking of itas like breaking down silos
as in like helping differentgroups throughout an enterprise
understand one another.
I like the framing of, it'skind of everybody's problem.
It is a team effort.
It's not just about kumbaya.
Do we all understandeach other and get along?
It's also that when it comesdown to like data governance
(06:17):
and compliance and security,like everyone is in some way
accountable for their own piece,and everyone does need to be
sort of singing from the samesongbook and that whole idea
of aligning and agreeing on.
What the impact or outcome isof that alignment needs to be
agreed and understood at theleadership level as well, so
that it's not just this sortof cultural shift, which is a
(06:39):
vague word that we are using allthe time, especially around AI,
but actually it is about risk.
It's actually about theROI, I guess, on having that
togetherness, having that unity.
Also the cost of gettingthat wrong, not having those
conversations and then incurringa risk or having a risk
realized that could be a veryexpensive and damaging risk.
(07:01):
That could probably beavoided if people were
kind of on the same page.
I like how like tactile that is.
Deborah Ketai (07:07):
It can be both squishy and tactile.
Galen Low (07:10):
I like that.
Squishy and tactile andthe tech component too.
Deborah Ketai (07:13):
She is a kind of tactile, so
that's, there's that.
Galen Low (07:16):
I love that.
I wonder if we couldzoom out a bit.
You have been, as you werealluding to, like your
previous role at a large UShealthcare group, you know,
your responsibility was actuallyfor leading change management
programs and initiativesrelating to a lot of things,
and I think most recently,AI and machine learning.
And it sort of comes back to,you know, this curiosity I have,
just what did your day-to-daylook like in this role and what
(07:40):
outcomes were expected of you?
Deborah Ketai (07:42):
As often happens with change management, my
biggest effort was probablythe upfront effort of defining
what the goals were, whatthe success metrics were
going to be, who was goingto be involved and so on.
And we had really threegoals — talent, mobility and
(08:06):
retention across the AI space.
So this is a hugeFortune five company.
AI is happening inpockets all over.
And let me be clear that when Italk about AI in this context,
it's not just generative AI,it's also predictive analytics.
It's wearables and sensors,it's Internet of things.
(08:27):
My second goal for the programwas to encourage collaboration
between these pockets of AI.
So that they're notreinventing the wheel.
And the third is a culturalshift, encouraging business
champions to considerAI solutions to their
business challenges.
So in the beginning,I spent a lot of time
(08:48):
identifying and segmentingthe stakeholder groups.
I partnered with an AIreporting and analytics
specialist in order to do this.
And determine their needs, findout how best to reach them.
And then the rest of the programprimarily involved creating
(09:12):
opportunities for them to learn,share, and build community.
Targeted to the differentgroups that those segments
that I identified, so myprojects within the program.
Were threefold events suchas an internal conference
and hackathons contentlike podcasts, articles,
(09:34):
presentations and community,internal social media
and knowledge sharing.
And then there was afourth piece to this,
which was risk managementadvising the governance
committee on governance,compliance, and security.
Galen Low (09:50):
That is a big program.
Yeah.
It's funny because we often talkin my community about like, you
know, getting sort of handed adirective and having to execute
it, but you know, you werestarting from further back.
And not only that, but evenwhat's interesting about it is
the timeframe, you know, youhad called out that it wasn't
just generous of AI, in fact.
At the time, generative AI wasrelatively new in the business
(10:11):
world in terms of like everydaybusiness application, but
this is more like foundationalchange, anticipating that there
will be greater change and morespecific change in the future.
It was more about sort ofbringing people together
and it's really interestingyou've done it again, right,
where like community canbe a squishy thing, but
actually the ROI and therisk management, I guess, of.
(10:34):
Bringing people togetherso that they understand one
another so that they cancross train, so that they can
collaborate together, likeit builds that foundation
to then do whatever is next.
That in itself isrisk management.
I really like that ideabecause I think the word
community of practice, evenjust the word community at
all gets thrown around and Ithink the value of community
(10:55):
isn't broadly understood.
It's not as scientific as youknow, a lot of business leaders
and executives would like.
But I think you've kind ofhit the nail on the head
that by doing these things,you're creating a sort of
fabric and unity and cohesionbetween disparate groups that
understand the world a differentway and trying to kind of
get them on the same page.
(11:17):
I really like that.
I was thinking abouttwo things, actually.
One, I was thinking about whatyou had mentioned about business
leaders and getting them alignedwith like how to use this
technology in the business.
It's funny.
Talking about it now in 2025when everybody is chasing
down every AI tool, but likedid you find that you were
(11:37):
convincing people that AI wasactually a worthwhile thing
to invest in and discuss?
Deborah Ketai (11:43):
Yes.
I think that there weretwo levels of that.
One was that very often.
Business leaders simplyhad not considered AI as a
potential solution to problems.
Whether those problems werefraud within healthcare or
diagnostic imaging, therewere so many different
(12:05):
potential areas where AIcould be used and they just
weren't thinking about it.
The second step was then reallyto help connect them with
people who were knowledgeableenough about it, that they
could boil the AI conceptsdown for them and explain,
(12:29):
alright, does this exist yet?
Can it exist in the near future?
And what are the risks,expenses, et cetera,
and potential return ofusing these solutions.
And then compare it becauseAI is never going to be the
one size fits all, you know,golden key to the puzzle.
(12:53):
So they still had toconsider their other
non-AI solutions as well.
Galen Low (12:58):
It's really interesting that like it
brings into such sharp reliefthe importance of talent.
And that's the first thingyou mentioned, right, is you
know, you actually took adata science ish analytical
approach to identifying thetalent and stakeholder groups
across the enterprise tounderstand the groups you're
dealing with and the skillsets that they have and the
(13:21):
skill sets that are needed.
To facilitate this likeknowledge sharing so
that everybody can kindof like level up, right?
It almost like lifts all boats,especially in that realm of
like the art of the possible iswhat we used to call it, right?
Is we need to know whatthis technology can do.
We have to understand thatit's not a silver bullet.
We have to understand how thisfits into the ecosystem of other
(13:41):
technology or other methods.
And on top of that.
Like in healthcare, thestakes are very high.
You know, there is a lotof compliance to consider.
There's a lot of, you know,regulation to consider, and
fundamentally we're talkingabout the livelihood of
human beings as the business.
That's what's at stake thereand that's why it's worth it to
bring all these groups togetherand identify who knows the
(14:03):
right things and who can trainand cross train one another
so that we make a strongerteam that sort of understands
one another, has that empathyhas worked together and can
pursue some of these, you know,solutions that might be AI like
only or might be AI combinedwith other technology, or might
be AI combined with differentmed tech or just process.
(14:24):
Gosh, what a big lift.
Out of curiosity, 'cause itis such a big program, how do
you even begin to measure thatchange to know if it's working?
And just as a backdrop, myorganization, you know, we
struggle, we want to get,bring everyone together, right?
We want to havethese hackathons.
We did a week long shutdownjust a few months ago to,
you know, explore as teams,different AI solutions, you
(14:48):
know, different approaches tohow we can use vibe coding.
But all along theway we're like.
Is this gonna be worth it?
We're gonna shut down.
We're gonna spend all thistime and energy to like have a
kind of, you know, an outcome.
That was pretty clear.
We also struggled with that.
How do we know if it worked?
How do we know if it's working?
What kinds of things wereyou measuring or asked to
measure in terms of whetheror not these, like cross
(15:10):
training and educationalsessions were working, or
the hackathons were working?
Deborah Ketai (15:13):
I will acknowledge that the metrics
were the most difficultpart of the program for me.
And a lot of that had to dowith the fact that some of
the data that we wanted totrack simply was not captured
in our HR and other systems.
So for example, not onlywas there no way to track
(15:37):
career mobility within the HRsystem, but because this was
a Fortune five company thathad grown largely through
mergers and acquisitions.
The relics of old HR systemsstill existed in the fact that
the same type of role oftenhad 15 different titles, job
(16:02):
titles or job codes acrossthe organization, and that
made life very difficult.
So we really often had to relyon very simple or proxy metrics.
Not the kinds of things thatyou'd wanna do long-term.
Long-term, we would've workedwith HR to capture more data,
(16:26):
but just simple things likeengagement with content.
So we implemented various kindsof web analytics on both the
social media, the website,and so on the microsites.
We also obviouslycaptured things like
conference attendance.
We were very much interestedin making sure, kind of as
(16:51):
a side note, that as we weredeveloping the AI practitioner
community, that it be diverse.
And so that wound up requiringall kinds of data, even as
we were inviting speakersfor this three day four
track conference, making surethat the various presenters.
(17:13):
Were different genders,different geographic locations
globally, all kinds ofdiversity metrics that we
also had different ages.
And again, a lot of thatdata was kind of hit or miss.
A lot of it was justplain missing and some
of it was misleading.
(17:33):
So had the program gone onlonger and unfortunately it,
it was cut short for a varietyof reasons, having nothing to
do with the program itself.
But had it gone on longer,I think we would have been
able to come up with ways ofdetermining and tracking data
that was more appropriate.
Galen Low (17:54):
I like that approach of like, what have
we got to work with and howcan we use that to gauge
traction, I guess is kind ofthe way I'm thinking about it.
Right now, I work in anorganization where we can
still wrap our arms aroundour staff and like, not
physically, but what I meanis like we can still, we
know that this individualover here is good at x and y.
You know, we don'thave a deep HRIS.
(18:15):
This sort of skills matrixisn't deeply formalized.
We just kind of know likethis person would be good.
They know their stuff.
Maybe they can doa lunch and learn.
When you talk about enterpriseat scale, and I'm thinking about
my time working for consultancy,like 500,000 employees, right?
It's almost more similar,like when you talk about
social media and these likeevents and these conferences,
it is almost more like asort of mass business model.
(18:38):
Like you would, if you wereplanning a conference that was
external or you know, buildinga social media platform and
you're looking for engagement,you're looking for things like.
Ticket sales and attendance.
You have to look at thisdata in aggregate, and then
you're hoping that they fillout that survey of on their
way in or on their way outabout who they were and who
they are and what things theylike to do, and that's the
data you have to work with.
(18:59):
I like that idea that if itcontinued on, absolutely, and
I think even just like havingan HR system that has more
structured and clean data aboutpeople and what they're good
at to sort of make decisionsabout community and events,
but also just like staffing.
I think it's the right idea.
You almost need AI to buildthe next generation of
(19:19):
stakeholder identification,I guess, at that scale.
But I like that it's actuallysort of signal based, right?
Not trying to dig too deepinto data you don't have and
wishing that you had it andsort of being immobilized by
it versus like being creative,working with what you've got.
What you said about thejob titles and the job
codes is like, what a mess.
Like I've worked inorganizations that, you
(19:41):
know, are like m and a isa weekly ritual, right?
Just like a choircompany every week.
And it's so messy andthere's so many things and
we're like, how do we evenknow who we've got now?
But yeah, I can sympathizewith that effort of trying to
figure out who does what and whoshould be at the table and who
could speak at this event andtrying not to like miss anybody.
(20:02):
Right?
Like why didn't you ask, youknow, like this person who is
our expert on this, I was like,wasn't in the system, sorry.
Deborah Ketai (20:08):
Right.
And you know, on the otherhand, whenever you're working
with a technology associatedchange, you have the advantage
of having user statistics ofvarious kinds and analytics of
various kinds that at least youcan call on to find out who's
using hugging face or whatever.
Galen Low (20:29):
Is hugging face a tool?
Deborah Ketai (20:31):
Hugging face provides these small
areas to create your owngenerative AI models.
Galen Low (20:40):
Okay.
Right, right.
My head went directly toRidley Scott's alien franchise.
It's like aliens that jumpup and grab your face.
I was like, probablynot what Deborah means.
Deborah Ketai (20:50):
Probably what the founders envisioned, right?
Galen Low (20:53):
Yeah, exactly.
Could we just have a thingthat jumps out at the
screen and just like slurpsdata out of their brain?
It strikes me that you're, youknow, you're very passionate
about change management and it'snot just AI change management.
You know, you've workedon de and I initiatives,
you've worked on othersort of data initiatives.
I'm just curious, like inyour experience, does change
management need to be treateddifferently when it comes
to AI, or is it kind of thesame playbook as other types
(21:15):
of organizational change?
Deborah Ketai (21:17):
There are some differences.
First of all, anythingrelated to AI and ML is
going to move faster andfaster as time goes on.
The learning curveis just exponential.
I think that people whoare going to manage change
(21:38):
initiatives or projects orprograms in the AI space need to
get a sense of the foundationsof data process, infrastructure,
alignment with strategic goals,and for change management.
You always needbuy-in at the top.
(21:58):
You need buy-in from middlemanagement because they're gonna
provide the what's in it for me.
And you need mechanisms forthe ground level employees
to feed ideas back up andattitudes back up to the top.
There's also the questionof resistance to AI as a
(22:18):
concept, whether it's the.
Immediate reflex of,is my job secure?
And the answer to thatis maybe, maybe not.
Is anybody's job securein this day and age?
There's a lack of understanding.
There is often a lack oftransparency, both around the
(22:40):
use of AI within a company andsimply the lack of transparency
of the tools themselves andhow they're making decisions,
or if you're gonna allow themto make decisions, how they're
coming up with their output.
There's plenty of fear andrisks involved, and so I think
that there is a separate spacewithin change management for
(23:01):
people who are working on AIrelated change initiatives.
But I think the basictenets of change management
are the same, regardless.
Galen Low (23:12):
That makes sense to me in terms of, yeah, the
things you mentioned aboutbuy-in and transparency.
Yeah.
What really got me was the paceof change and that it's not just
like one change in organizationsthat I've worked in the past.
There is like a changemanagement team, right?
And they're gonna managethat one change and
then they walk away and.
Come back wheneverthey're needed, right?
For the next big change.
(23:33):
But what we're talking abouthere is actually continuous
change on sort of that like, Idon't wanna say shaky ground,
but you know, I think the thingyou said about job security and
fear of the technology and likethe black box decision making
within the technology, those areall things that sort of amplify
how people experience change.
(23:54):
Then it's exacerbated bythe fact that it doesn't
just end, you know?
I'm like to take the metaphortoo far, I'm picturing
like hunger Games, right?
You're like, okay, maybe ifthere's one, like one round
and you make it, you'relike, okay, I'm safe forever.
Whereas that's not the case.
It's gonna like kind of,it's continuous change.
I once got chided for sayingconstant change 'cause
constant, anyways, but thecontinuous change aspect
(24:16):
is a thing that makes it.
Complicated because it's notjust a straight line anymore,
like linear change management.
It's like, it's cyclical.
It's like a cycle of changethat almost needs to be managed
continuously because changeisn't, you know, hasn't stopped.
Deborah Ketai (24:31):
Right.
I mean, even withinthe AI, the individual
AI efforts themselves.
There's continual change.
Your data is driftingas time goes on.
So there are all kinds ofdifferent things that the
project manager or the changemanager or both need to take
into account that are differentbased on the science involved.
(24:52):
But as I said, that'skind of a small area
relative to the foundationsof project management
and change management.
Those are the same.
Galen Low (25:02):
Yeah, the frameworks are the same.
The stuff that goes insideof it, there will always be,
well, it couldn't make it tooeasy for ourselves, could we?
It's not gonna bethe same every time.
There's gonna be nuance, there'sgonna be complexities about
what type of change it is.
Deborah Ketai (25:16):
And one of the complexities that I think
people are just now starting torealize is that increasingly,
particularly with generativeAI, AI is itself a stakeholder.
It has its own.
You know, we're finding outthat it has its own goals,
its own attitudes, and itwill act differently and
(25:40):
sometimes contrary to theway you want it to based on
those internal realities.
Galen Low (25:46):
That's interesting.
I really like that.
We've been talking in thecommunity about almost like
adding your AI tools to thelike project communication
plan because it becomes asource of truth or you know,
a source of information.
So we do need tocommunicate updates to it.
You know, whether that'suploading meeting minutes
and stuff up to like thetools that we're using so
that they're up to date.
(26:06):
I like that whole notionof like, they've got
their own attitudes andperspectives on things.
And you know, this time twoyears ago, it would've been
like, that's sci-fi, but it'snot really, you know, we're
talking about agentic workflows.
We're, you know, we're talkingabout pursuing a general
artificial intelligence.
We're not there yet, but evenjust now, we do need to kind of
consider that because frankly,that's how it's built, right?
(26:29):
To either a, especially in thecase of generative AI, cater to
an individual user's preference,but maybe also behave
differently in front of anotheruser, which I guess is probably
the same with humans, but Ilike that idea that it needs
to be taken into account, thatit is a stakeholder, that it
itself is changing and it itselfneeds change management in a
(26:51):
way to understand the changethat are going on around it.
Deborah Ketai (26:54):
I think, you know, it's very much in my
mind, like belief in God.
I don't think you haveto believe in God.
I don't think you haveto believe in general
artificial intelligencein order to recognize.
That there are ways in whichthe world, or the ways in
(27:15):
which artificial intelligenceacts as though it had a
personality or as thoughthere were divine design.
And so you need to take thosethings into account and see how
you can limit guide, et cetera,or risk being overtaken by
(27:39):
events and situations that youdon't have any control over.
Galen Low (27:43):
I feel like we need to make some t-shirts that says
AI works in mysterious ways.
It's just not untrue.
You know, like all along hereI'm thinking of, you know,
you and your background,your role as sort of program
manager and change manager.
You are deeply involvedwith the project management
community and in my community.
(28:03):
There's some folks whoare being asked to kind
of do a dual role, right?
They're being askedto be project managers
and change managers.
In other words, it's hybridizingthis sort of PM and CM roles,
and it seems to be, at leastfrom where we stand, it seems
to be getting more common.
I wanted to get your takebecause you've kind of lived.
I guess first of all, likedo all projects need change
(28:25):
management and also do allproject managers need to
understand change management?
Deborah Ketai (28:30):
No, not all projects require
change management.
The orthodox view on thatis basically that a project
that is complex and that has.
A risk of not realizing itsbenefits if people fail to
adopt it once it's implemented.
And I'm not just talking abouttechnology projects there.
(28:54):
Those are the projects thatneed change management.
I don't think that it'sreasonable to expect project
managers to be change managers,but it is becoming the norm
in a lot of organizations.
The skill sets.
Are different but overlapping.
But what I tend to focus on isthe reason why you should have
(29:18):
separate roles is the timelines,because traditionally, project
managers are not given theopportunity to follow through
and really make sure thata change gets sustained.
Galen Low (29:33):
Interesting.
Yeah.
Deborah Keta (29:34):
That's really key.
So unless your PMO or operationsor whoever is governing your
project management processis willing to recognize
and fund the ability ofthe project managers to
ensure change sustainment.
(29:57):
I don't think it makes senseto have project managers
do change management.
But yes, they have tounderstand each other.
I think it's going to becomemore and more the norm and
more and more helpful to haveintegrated project and change
management plans and forproject managers who either
are forced to or are interestedin doing change management.
(30:20):
Some of the things that would bereally helpful to them to learn
some are our basic concepts.
To understand that successfulchange has to come from the
top, the bottom, and the middleof the corporate hierarchy.
To understand that, dependingon your organization, you may
need to be able to step intothe role of communications lead.
(30:41):
You may need to work withyour company's learning and
development function, oreven play that part yourself.
If your company doesn't haveone, you may need to establish
or re-engineer processes.
So things that are notnecessarily a strong focus
of most project managers.
They're there in projectmanagement, but they're
(31:03):
not really a focus tothe extent that they
are for change managers.
And the otherthing is that both.
Individual project managers andproject management organizations
need to be willing to andcommit to putting in a lot
of time and effort upfront onvarious kinds of assessments.
(31:24):
So stakeholder assessments,impact assessments,
readiness assessments.
Without those, the changeis not gonna be successful.
The adoption's not goingto be successful, or it's
going to be successful atfirst and then slip back.
Galen Low (31:38):
I like that sort of time aspect of things.
And even these sort of, youknow, up top, when you were
talking about your mission,your, you know, day to day it
almost was so zoomed out andfoundational and like almost.
It was in itself an initiative,which arguably is a project,
but also it probably set thefoundation to spawn other
projects, like for example.
Coordinating the hackathonwould be a project, right?
(32:00):
That you would have someoneideally come in and take the
lead on and deliver, you know,they could be in and out.
You did all this sort ofupfront foundation work
to sort of set up thechange and then afterwards.
You know, there's a periodof time longer than a project
where change is measured.
What I find interesting aboutit is that, you know, when
we're talking to the folksat the Project Management
(32:20):
Institute, one of the sortof big focuses now is driving
outcomes and doing more thanjust managing the iron triangle
and being more responsiblefor the value that we deliver.
And I think that comes up asone of the arguments as well
is like, but we're not alwaysaround to see that change
manifest and like, you know,are we setting ourselves up for
success if we're actually justmoving onto the next project
(32:44):
and actually have nothing todo with, you know, how that
impact is measured over time.
You know, are thosethings even compatible?
I don't even know if thisis a question in there.
Deborah Ketai (32:55):
Could it, I think there are a couple
of thoughts in there.
One that comes to mind isthat in the initial focus
on Agile, one of the coretenets was dedicated teams.
Well, that was one of thefirst things that slid Yes,
by the wayside when it wasactually implemented in
(33:16):
most large organizations.
And the fact is somebodyneeds to be dedicated to
following up on all this.
You can call thempart of the team.
You can call them a teamthat sits kind of above
the fray and communicates.
With the teams that come andgo, but somebody has to do that.
Galen Low (33:37):
I like that you framed it earlier
as an investment.
In other words, you know, aleadership team must be willing
to invest in having someonewho is that dedicated person
or team that oversees it.
And you know, I'm thinking aboutmy time in consulting or you
know, where we did have, it wasa whole separate division that
did sort of change management.
As a separate layer, and nowI'm starting to understand
(33:58):
why is because, you know,their aspect was different.
It started earlier and it endedlater in terms of like managing
change, measuring, you know,the impact of that change.
And then there's this sort ofthe middle bit right where the
project got done and deliveredand it would be a. Probably
non-viable investment for abusiness to say no, we'll just
keep that project team on tolike sit around and measure
(34:21):
their results for the next 18months and not do anything else.
Right.
And we need them tosort of shift along.
But I like that idea thatthe investment can be in a
dedicated person or a dedicatedgroup that oversees change
and that, you know, maybethe recommended direction
is not that project managersalso be change managers.
But that perhaps they couldswitch hit, like maybe you
(34:43):
could be the change managerrole in a certain initiative and
you could also be the projectmanager, maybe one at a time.
Not both at the same time,because yeah, it's hard
to be responsible formeasuring impact if you just
roll onto another projectafter a project goes live.
I guess maybe that kind of like.
It kind of triggers me tosort of zoom out a bit and
(35:06):
I'm thinking about, youknow, the role of a project
or a program manager.
From your perspective, how doyou think the role of a program
manager will evolve over thenext three to five years?
And maybe like, what skillsshould PMs be focusing
on today so that they canbe ready when it does?
Deborah Ketai (35:21):
For program managers and by program manager
I'm going to define it just forthe purposes of this question.
As somebody who manages a setof related projects and as a
set, the program may or may nothave defined start and end date.
(35:44):
It may be somethingthat's ongoing.
I think that program managersneed to get closer to where
strategy is formed and beable to influence strategy.
And to get senior leadership toclarify priorities, goals, and
(36:07):
to be transparent in reflectingback to leadership where there
are likely to be misalignments,where investments are not likely
to yield the appropriate return.
Where that return is likelyto be important in the
long run, but no guys, it'snot gonna give you 10 X
(36:30):
in the next fiscal year.
But it's still important.
I also think that programmanagers are going to need
to focus more on enterpriserisk and by risk I'm including
the classic, both negativerisks and opportunities.
Galen Low (36:45):
I love that you did that because
not a lot of people do.
Right?
The like opportunistic,like positive risks.
And honestly, I think thatmakes sense, especially
with that definition.
I'm glad you defined it upfrontbecause I'm kind of thinking
of that role where you're aprogram owner, your program
does not start and stop.
It's not just like a collectionof projects that you know
will end at a certaindate, but you actually
(37:06):
sort of own this program.
You have that perspective,right, to be looking beyond
a certain date to be, youknow, thinking of the business
strategy, I guess my questionwould be, have you found that
leadership teams want programmanagers at the strategic table?
Or are program managers who aretrying to be more strategic,
kind of like fighting toothand nail to get invited in?
(37:28):
And if so, what's thejourney look like there?
Deborah Ketai (37:30):
I'm going to use the classic
project manager response.
It depends.
It depends on theleader, it depends on the
organizational culture.
And I also think a lot oftimes we have to be proactive.
We can't just assume thatif we're not invited in
early, that it's becausethey don't want us there.
(37:53):
Sometimes it's justnobody's thought about it.
So over the course of thenext 3, 5, 10 years, building
relationships is going to becomemore and more an important
function of project and programmanagers, both because of.
AI impact on jobs in general.
(38:16):
And because they're going toneed to get closer and closer to
that source of power and vision.
And I think another thing thatis going to happen over the
next three to five years isthe most successful project and
program managers are going to bethe ones that kind of blur the
(38:37):
lines between business and it.
The ones that are coming froman IT background are really
going to have to learn whatmakes their business tick and
what makes their industry tick.
The ones that are coming froma business background are going
to have to learn somethingabout what enables the different
(38:58):
aspects of their business toshare data and information.
If it's healthcare to, you know,to process claims, to collect
money, they have to understandthe basics of all those
things, and so that line isgoing to blur as time goes on.
Galen Low (39:16):
I like that sort of almost how
pragmatic it is, right?
I think there's a lot ofLinkedIn posts and other sort
of media that's almost like youneed to flip the switch tomorrow
and become a strategic leaderand be at the table, you know?
Like now.
But I appreciate that,you know, your view.
It will take time, right?
You need to kind of be thatperson who's flagging risks
(39:37):
and showing that you understandthe business or showing that
you understand the technologyin your current role, and
then sort of stretch up andblend into that next role.
And I think that's areally, like a realistic
view of how this shiftis actually happening.
You know, everyone's like,well, it's gonna, AI is
gonna level everyone up, andthen we kind of stop there.
But none of us reallyknow what that means, but.
(39:59):
That's what it means.
It means, okay, like startbuilding these skills now.
Start buildingthese relationships.
Now, it's not that you aren'twanted at the table, it's just
that people don't know thatyou should be at the table yet.
That'll take time, and then yes,you will have this perspective.
Ideally the bandwidth tohave that perspective, to
look at it from a businessstrategy and tech strategy
(40:22):
lens, not just a projectscope, project budget lens.
Deborah Ketai (40:26):
Right and I mean, think of it in terms of your
own life as a project manager.
You already know that thereare other people who are not
invited in early enough, whetherit's the testing team, the
compliance and security folks.
We all have this problemof not being in early
enough in the conversation.
(40:47):
And if you need a wedgeinto that world, sometimes
it's helpful just to ask,to be invited to meetings
and promise you'll justbe a fly on the wall.
You are not going to engage,you're not, you know, whatever.
But at least be there and haveskip level meetings with your
(41:10):
managers so that you can startpercolating some of your ideas
up or some of your concerns up.
Galen Low (41:17):
I like that It almost brings us full circle
to what you're talking aboutat the beginning, which is
sort of getting the rightperspectives and talent in a
room together, or have themexchange knowledge and cross
train and sort of buildingcommunity because you know, if
we all are able to see it fromone another's perspectives.
Or even if we all start tosee things a little bit from
the same perspective, that'sa tie that lifts all boats.
(41:39):
You know, like that's whatcreates that cohesion.
That's what gives us theability to work together
cohesively, to address thechanges in front of us, address
the challenges in front ofus at speed and at scale.
Deborah Ketai (41:51):
I agree.
Galen Low (41:52):
Nice little bow.
Deborah, thanksso much for this.
Just for fun, do you have aquestion that you wanna ask me?
Deborah Ketai (41:59):
I do.
I don't know if you're going towanna answer it, but what is one
thing that you would love to dowith AI but are either afraid
to or don't know how to do?
Galen Low (42:07):
Oh my gosh.
Yeah.
The list is many, but I'm verysatisfied with the way a lot
of my generative AI tools workas like a thought partner,
and I haven't yet sort ofunleashed it to be autonomous
and not just agentic workflows,but like thinking of what I
would do if I had a projectcoordinator or an assistant
(42:28):
that's a human on my side tokind of take a ball and run
with it with limited direction.
And I know that's sort of whatpeople are trying to build,
but the way I'm seeing itright now is that a lot of
the agentic workflows havemore to do with intelligent
automation than they do.
Like sort of taking action.
It's almost like this blend,like I wish I could sort
(42:48):
of quote unquote prompt myassistant to go and do a few
things across different tools.
And I'm thinking of things likeagent mode in ChatGPT, and maybe
I need to play with it more.
But it is this sort of likecross tool administration that
is sort of by request, likeas it happens, not necessarily
sitting in the background asan AI agent doing its job.
(43:10):
I just crave a sort of moremultipurpose assistant who
is somewhat autonomous andsomewhat prompted like a
bit of that blend to kind ofgo out and just shave some
of that time off for me.
You know, I dunno if that's likea very vague answer, but I just
find myself sitting in betweenthe folks who are building
(43:32):
apps, you know, vibe, codingapps, folks who are building
agents that just work in thebackground and do stuff and
make decisions on their own.
And prompting, which is notquite the same for me as having
a. If I had a human assistantto go out and tackle some tasks
for me, that would be great.
I will prompt them.
They will make somedecisions themselves.
They'll come back.
You know, maybe that ishow people are building
(43:54):
agents right now, but Ijust haven't seen it yet.
Deborah Ketai (43:56):
Yeah.
What keeps me from doing it,frankly, is lack of trust in
where my data is going to go.
Galen Low (44:02):
Is that black box again.
Maybe this is a whole otherepisode, but you know, I think
that is one of the thingsthat gives everyone pause.
I was reading an articleand I can't remember where
it was for the life of me,but if I do remember, I'll
put it in the show notes.
But this notion of not adoptionslippage, I can't remember
the word, but it's just likepeople are kind of like hitting
a wall somewhere where they'relike, start using AI and then
(44:26):
at a certain point, you know,only a certain percentage is
using it for these types ofthings or at this frequency.
And I think one of the things,it's not necessarily just.
The technology or understandingit, but like the trust and
also the transparency arounddata governance and compliance
and where does this go andwhat am I allowed to do and
you know, can I connect?
(44:47):
Can I plug all these thingstogether and will it be safe?
Even just that thought, noteven to going through the
process, but even just thatthought creates hesitation.
But yeah, I relateto that completely.
That's me as well.
I'm like, what ifit does it wrong?
What if it's, you know,sending data everywhere?
This all kind ofhappens in a black box.
It could go sideways onme before it actually
creates a benefit.
Deborah Ketai (45:05):
I was recently at a PMI Regional Leadership
Conference, and one of thesession breakout sessions was
about AI and project management,and one of the things that
came up was the question, is itethical to have your chapters
(45:27):
email blasts facilitated by AI.
Galen Low (45:31):
Interesting.
Deborah Ketai (45:32):
And the outcome of the discussion
was that basically it wasnot ethical if it meant
releasing your members' data.
Galen Low (45:45):
Right.
Okay.
Deborah Ketai (45:45):
Into the void.
So unless you were using somekind of completely internal
tool, the answer was no.
Galen Low (45:52):
Isn't it interesting how it always kind of comes
back to guardrails that weourselves as humans, don't
fully understand, right?
They're discretionary decisionsusually, and then there's,
you know, policy and the sortof governance aspect, but
we don't yet trust ourselvesand our technology to kind
of build those guardrailsand put them in place so
that we have that trust.
(46:13):
Right.
That's very interesting.
I'd love to revisit that.
I'm always, I'm interestedin ideas and examples of how
AI is being used in projectmanagement and otherwise and
also what gives us pause,what obstacles we're facing.
Often it's not the technology,it's actually the ethics.
Deborah Ketai (46:30):
Yes.
Galen Low (46:31):
And the trust.
That's so interesting.
Deborah, thank you so much forspending the time with me today.
I really enjoyed myself.
For folks listening, where canpeople learn more about you?
Deborah Ketai (46:40):
Definitely connect with me on LinkedIn.
I'm the only DeborahKetai on LinkedIn.
And if you have theopportunity to add a note,
mention the DPM podcast.
Galen Low (46:51):
Awesome.
I will include a linkto your LinkedIn.
I'm happy that you'rethe only Deborah Ketai.
I'm like one of two GalenLows on LinkedIn, so
I have envy right now.
But I'll include that linkin the show notes for folks
to connect with Deborah.
Deborah, thank you again.
Deborah Ketai (47:04):
Thank you so much, Galen.
Galen Low (47:06):
That's it for today's episode of The Digital
Project Manager Podcast.
If you enjoyed thisconversation, make sure
to subscribe whereveryou're listening.
And if you want even moretactical insights, case studies
and playbooks, head over tothedigitalprojectmanager.com.
Until next time,thanks for listening.
Is it worth the effort to try to
break down silos and alignattitudes around AI within
a massive organization?
Is it even possible?
Deborah Ketai (00:08):
If they skip the step, they're going to
incur a lot of debt, technicaldebt, knowledge debt that is
gonna make future projects moreexpensive and more difficult?
Galen Low (00:20):
You had called out that it wasn't just generative
AI, in fact, this is morelike foundational change.
Deborah Ketai (00:25):
We had really three goals — talent, mobility,
and retention to encouragecollaboration between these
AI teams so that they'renot reinventing the wheel.
And the third is a culturalshift, encouraging business
champions to considerAI solutions to their
business challenges.
Galen Low (00:44):
Some folks who are being asked to do a dual role.
Do all projects needchange management?
Deborah Ketai (00:49):
Not all projects require change management.
The skillsets aredifferent but overlapping.
Program managers need toget closer to where strategy
is formed to be transparentin reflecting back to
leadership where they'relikely to be misalignments.
Galen Low (01:15):
Welcome to The Digital Project Manager
podcast — the show thathelps delivery leaders work
smarter, deliver faster, andlead better in the age of AI.
I'm Galen, and every week wedive into real world strategies,
new tools, proven frameworks,and the occasional war story
from the project front lines.
Whether you're steering massivetransformation projects,
wrangling AI workflows,we're just trying to keep
(01:36):
the chaos under control.
You're in the right place.
Let's get into it.
Okay, today we're peeringbehind the curtain of how a
program manager and her teamcreated a community of practice
within a massive healthcareenterprise that helped remove
silos and align attitudesaround AI within the business.
My guest today is that veryprogram manager, Deborah Ketai.
(01:58):
Deborah is a certifiedorganizational change
manager and program managermost recently for a massive
US-based healthcare group,where she built thriving
communities around AI, DEI,and data transmission controls.
Deborah has also been veryactive in building the
project management communityas a board member and
programs portfolio lead fora regional chapter of PMI.
(02:19):
In this episode, we're gonnacover what Deborah's day-to-day
looked like as a program managerleading a major organizational
change initiative around AI,whether AI and machine learning
related organizational changeneeds to be treated differently,
what teams of any size canborrow from the playbooks
of enterprises undergoingmassive AI transformations,
and whether the role of aproject or program manager will
(02:42):
evolve to incorporate changemanagement and what that might
mean for the PM skillset.
Deborah, thank you somuch for being here today.
Deborah Ketai (02:49):
Thank you for the opportunity, Galen.
Galen Low (02:51):
I'm so excited about this conversation because you
are just someone who has beenlike very deeply involved and
embedded as a program lead andchange manager for a massive
healthcare enterprise just as AIwas beginning to enter the fray.
In our conversations leading upto this, you've mentioned things
like breaking down silos betweendivisions and stakeholder
groups, running trainingsessions and hackathons,
(03:14):
addressing fears around riskand compliance, and also just
getting AI practitioners andmaybe the AI practitioner
wannabes all on the same page.
So I thought maybe I'djust start with like one
hot question that I thinkmy listeners want to know.
My question is this.
Is it worth the effort andenergy to try to break down
silos and align attitudesaround AI within a massive
(03:34):
enterprise organization?
Is it even possible?
And what would happen ifan organization like the
one you were working withskip that step completely?
Deborah Ketai (03:42):
That's really a good question, Galen,
and I'm glad you asked it.
I think alignment is going to becritical and breaking down silos
is gonna be a big piece of that.
First of all, companies aregoing to need to establish
data and AI governance.
They're going to needto align their resources
and expectations arounddeveloping or buying AI tools.
(04:06):
And if they want to have an AIfirst culture, and they're going
to have to, if their competitorsare doing it, they're going to
need to think about what thatmeans for their employees,
both now and in the future.
So at a bare minimum, they'regoing to need to break down
silos to avoid the risk ofcybersecurity incidents.
(04:27):
If they skip the stepof breaking down silos,
they're also going toincur a lot of what we
call debt, technical debt,knowledge, debt, and so on.
That is gonna make futureprojects more expensive
and more difficult.
I think there are three areasin which I've broken down silos.
(04:49):
You really mentionedall of them.
AI, system operating controls,and as community engagement,
lead of the second largestemployee resource group
at United Health Group.
And also to some extentthrough a mentorship program,
breaking down silos aftermergers and acquisitions.
So I think that there area number of different steps
(05:12):
involved and skills involvedin breaking down silos that
project managers and changemanagers need to focus on.
One is creating empathyamong different groups
by familiarizingthem with each other.
Cross-training, job shadowing,mentoring and coaching.
Leadership needs to create andget buy-in on the vision of
(05:34):
what it would look like andwhat the benefits would be
of breaking down the silos.
They need to create andconcentrate on touch
points, processes, andcommunication channels that
bring the silos together.
And they also need toconcentrate on eliminating
obstacles to bringing peopletogether such as different data
sources is one of the big ones.
Galen Low (05:56):
What I really like about your answer there is,
I was kind of thinking of itas like breaking down silos
as in like helping differentgroups throughout an enterprise
understand one another.
I like the framing of, it'skind of everybody's problem.
It is a team effort.
It's not just about kumbaya.
Do we all understandeach other and get along?
It's also that when it comesdown to like data governance
(06:17):
and compliance and security,like everyone is in some way
accountable for their own piece,and everyone does need to be
sort of singing from the samesongbook and that whole idea
of aligning and agreeing on.
What the impact or outcome isof that alignment needs to be
agreed and understood at theleadership level as well, so
that it's not just this sortof cultural shift, which is a
(06:39):
vague word that we are using allthe time, especially around AI,
but actually it is about risk.
It's actually about theROI, I guess, on having that
togetherness, having that unity.
Also the cost of gettingthat wrong, not having those
conversations and then incurringa risk or having a risk
realized that could be a veryexpensive and damaging risk.
(07:01):
That could probably beavoided if people were
kind of on the same page.
I like how like tactile that is.
Deborah Ketai (07:07):
It can be both squishy and tactile.
Galen Low (07:10):
I like that.
Squishy and tactile andthe tech component too.
Deborah Ketai (07:13):
She is a kind of tactile, so
that's, there's that.
Galen Low (07:16):
I love that.
I wonder if we couldzoom out a bit.
You have been, as you werealluding to, like your
previous role at a large UShealthcare group, you know,
your responsibility was actuallyfor leading change management
programs and initiativesrelating to a lot of things,
and I think most recently,AI and machine learning.
And it sort of comes back to,you know, this curiosity I have,
just what did your day-to-daylook like in this role and what
(07:40):
outcomes were expected of you?
Deborah Ketai (07:42):
As often happens with change management, my
biggest effort was probablythe upfront effort of defining
what the goals were, whatthe success metrics were
going to be, who was goingto be involved and so on.
And we had really threegoals — talent, mobility and
(08:06):
retention across the AI space.
So this is a hugeFortune five company.
AI is happening inpockets all over.
And let me be clear that when Italk about AI in this context,
it's not just generative AI,it's also predictive analytics.
It's wearables and sensors,it's Internet of things.
(08:27):
My second goal for the programwas to encourage collaboration
between these pockets of AI.
So that they're notreinventing the wheel.
And the third is a culturalshift, encouraging business
champions to considerAI solutions to their
business challenges.
So in the beginning,I spent a lot of time
(08:48):
identifying and segmentingthe stakeholder groups.
I partnered with an AIreporting and analytics
specialist in order to do this.
And determine their needs, findout how best to reach them.
And then the rest of the programprimarily involved creating
(09:12):
opportunities for them to learn,share, and build community.
Targeted to the differentgroups that those segments
that I identified, so myprojects within the program.
Were threefold events suchas an internal conference
and hackathons contentlike podcasts, articles,
(09:34):
presentations and community,internal social media
and knowledge sharing.
And then there was afourth piece to this,
which was risk managementadvising the governance
committee on governance,compliance, and security.
Galen Low (09:50):
That is a big program.
Yeah.
It's funny because we often talkin my community about like, you
know, getting sort of handed adirective and having to execute
it, but you know, you werestarting from further back.
And not only that, but evenwhat's interesting about it is
the timeframe, you know, youhad called out that it wasn't
just generous of AI, in fact.
At the time, generative AI wasrelatively new in the business
(10:11):
world in terms of like everydaybusiness application, but
this is more like foundationalchange, anticipating that there
will be greater change and morespecific change in the future.
It was more about sort ofbringing people together
and it's really interestingyou've done it again, right,
where like community canbe a squishy thing, but
actually the ROI and therisk management, I guess, of.
(10:34):
Bringing people togetherso that they understand one
another so that they cancross train, so that they can
collaborate together, likeit builds that foundation
to then do whatever is next.
That in itself isrisk management.
I really like that ideabecause I think the word
community of practice, evenjust the word community at
all gets thrown around and Ithink the value of community
(10:55):
isn't broadly understood.
It's not as scientific as youknow, a lot of business leaders
and executives would like.
But I think you've kind ofhit the nail on the head
that by doing these things,you're creating a sort of
fabric and unity and cohesionbetween disparate groups that
understand the world a differentway and trying to kind of
get them on the same page.
(11:17):
I really like that.
I was thinking abouttwo things, actually.
One, I was thinking about whatyou had mentioned about business
leaders and getting them alignedwith like how to use this
technology in the business.
It's funny.
Talking about it now in 2025when everybody is chasing
down every AI tool, but likedid you find that you were
(11:37):
convincing people that AI wasactually a worthwhile thing
to invest in and discuss?
Deborah Ketai (11:43):
Yes.
I think that there weretwo levels of that.
One was that very often.
Business leaders simplyhad not considered AI as a
potential solution to problems.
Whether those problems werefraud within healthcare or
diagnostic imaging, therewere so many different
(12:05):
potential areas where AIcould be used and they just
weren't thinking about it.
The second step was then reallyto help connect them with
people who were knowledgeableenough about it, that they
could boil the AI conceptsdown for them and explain,
(12:29):
alright, does this exist yet?
Can it exist in the near future?
And what are the risks,expenses, et cetera,
and potential return ofusing these solutions.
And then compare it becauseAI is never going to be the
one size fits all, you know,golden key to the puzzle.
(12:53):
So they still had toconsider their other
non-AI solutions as well.
Galen Low (12:58):
It's really interesting that like it
brings into such sharp reliefthe importance of talent.
And that's the first thingyou mentioned, right, is you
know, you actually took adata science ish analytical
approach to identifying thetalent and stakeholder groups
across the enterprise tounderstand the groups you're
dealing with and the skillsets that they have and the
(13:21):
skill sets that are needed.
To facilitate this likeknowledge sharing so
that everybody can kindof like level up, right?
It almost like lifts all boats,especially in that realm of
like the art of the possible iswhat we used to call it, right?
Is we need to know whatthis technology can do.
We have to understand thatit's not a silver bullet.
We have to understand how thisfits into the ecosystem of other
(13:41):
technology or other methods.
And on top of that.
Like in healthcare, thestakes are very high.
You know, there is a lotof compliance to consider.
There's a lot of, you know,regulation to consider, and
fundamentally we're talkingabout the livelihood of
human beings as the business.
That's what's at stake thereand that's why it's worth it to
bring all these groups togetherand identify who knows the
(14:03):
right things and who can trainand cross train one another
so that we make a strongerteam that sort of understands
one another, has that empathyhas worked together and can
pursue some of these, you know,solutions that might be AI like
only or might be AI combinedwith other technology, or might
be AI combined with differentmed tech or just process.
(14:24):
Gosh, what a big lift.
Out of curiosity, 'cause itis such a big program, how do
you even begin to measure thatchange to know if it's working?
And just as a backdrop, myorganization, you know, we
struggle, we want to get,bring everyone together, right?
We want to havethese hackathons.
We did a week long shutdownjust a few months ago to,
you know, explore as teams,different AI solutions, you
(14:48):
know, different approaches tohow we can use vibe coding.
But all along theway we're like.
Is this gonna be worth it?
We're gonna shut down.
We're gonna spend all thistime and energy to like have a
kind of, you know, an outcome.
That was pretty clear.
We also struggled with that.
How do we know if it worked?
How do we know if it's working?
What kinds of things wereyou measuring or asked to
measure in terms of whetheror not these, like cross
(15:10):
training and educationalsessions were working, or
the hackathons were working?
Deborah Ketai (15:13):
I will acknowledge that the metrics
were the most difficultpart of the program for me.
And a lot of that had to dowith the fact that some of
the data that we wanted totrack simply was not captured
in our HR and other systems.
So for example, not onlywas there no way to track
(15:37):
career mobility within the HRsystem, but because this was
a Fortune five company thathad grown largely through
mergers and acquisitions.
The relics of old HR systemsstill existed in the fact that
the same type of role oftenhad 15 different titles, job
(16:02):
titles or job codes acrossthe organization, and that
made life very difficult.
So we really often had to relyon very simple or proxy metrics.
Not the kinds of things thatyou'd wanna do long-term.
Long-term, we would've workedwith HR to capture more data,
(16:26):
but just simple things likeengagement with content.
So we implemented various kindsof web analytics on both the
social media, the website,and so on the microsites.
We also obviouslycaptured things like
conference attendance.
We were very much interestedin making sure, kind of as
(16:51):
a side note, that as we weredeveloping the AI practitioner
community, that it be diverse.
And so that wound up requiringall kinds of data, even as
we were inviting speakersfor this three day four
track conference, making surethat the various presenters.
(17:13):
Were different genders,different geographic locations
globally, all kinds ofdiversity metrics that we
also had different ages.
And again, a lot of thatdata was kind of hit or miss.
A lot of it was justplain missing and some
of it was misleading.
(17:33):
So had the program gone onlonger and unfortunately it,
it was cut short for a varietyof reasons, having nothing to
do with the program itself.
But had it gone on longer,I think we would have been
able to come up with ways ofdetermining and tracking data
that was more appropriate.
Galen Low (17:54):
I like that approach of like, what have
we got to work with and howcan we use that to gauge
traction, I guess is kind ofthe way I'm thinking about it.
Right now, I work in anorganization where we can
still wrap our arms aroundour staff and like, not
physically, but what I meanis like we can still, we
know that this individualover here is good at x and y.
You know, we don'thave a deep HRIS.
(18:15):
This sort of skills matrixisn't deeply formalized.
We just kind of know likethis person would be good.
They know their stuff.
Maybe they can doa lunch and learn.
When you talk about enterpriseat scale, and I'm thinking about
my time working for consultancy,like 500,000 employees, right?
It's almost more similar,like when you talk about
social media and these likeevents and these conferences,
it is almost more like asort of mass business model.
(18:38):
Like you would, if you wereplanning a conference that was
external or you know, buildinga social media platform and
you're looking for engagement,you're looking for things like.
Ticket sales and attendance.
You have to look at thisdata in aggregate, and then
you're hoping that they fillout that survey of on their
way in or on their way outabout who they were and who
they are and what things theylike to do, and that's the
data you have to work with.
(18:59):
I like that idea that if itcontinued on, absolutely, and
I think even just like havingan HR system that has more
structured and clean data aboutpeople and what they're good
at to sort of make decisionsabout community and events,
but also just like staffing.
I think it's the right idea.
You almost need AI to buildthe next generation of
(19:19):
stakeholder identification,I guess, at that scale.
But I like that it's actuallysort of signal based, right?
Not trying to dig too deepinto data you don't have and
wishing that you had it andsort of being immobilized by
it versus like being creative,working with what you've got.
What you said about thejob titles and the job
codes is like, what a mess.
Like I've worked inorganizations that, you
(19:41):
know, are like m and a isa weekly ritual, right?
Just like a choircompany every week.
And it's so messy andthere's so many things and
we're like, how do we evenknow who we've got now?
But yeah, I can sympathizewith that effort of trying to
figure out who does what and whoshould be at the table and who
could speak at this event andtrying not to like miss anybody.
(20:02):
Right?
Like why didn't you ask, youknow, like this person who is
our expert on this, I was like,wasn't in the system, sorry.
Deborah Ketai (20:08):
Right.
And you know, on the otherhand, whenever you're working
with a technology associatedchange, you have the advantage
of having user statistics ofvarious kinds and analytics of
various kinds that at least youcan call on to find out who's
using hugging face or whatever.
Galen Low (20:29):
Is hugging face a tool?
Deborah Ketai (20:31):
Hugging face provides these small
areas to create your owngenerative AI models.
Galen Low (20:40):
Okay.
Right, right.
My head went directly toRidley Scott's alien franchise.
It's like aliens that jumpup and grab your face.
I was like, probablynot what Deborah means.
Deborah Ketai (20:50):
Probably what the founders envisioned, right?
Galen Low (20:53):
Yeah, exactly.
Could we just have a thingthat jumps out at the
screen and just like slurpsdata out of their brain?
It strikes me that you're, youknow, you're very passionate
about change management and it'snot just AI change management.
You know, you've workedon de and I initiatives,
you've worked on othersort of data initiatives.
I'm just curious, like inyour experience, does change
management need to be treateddifferently when it comes
to AI, or is it kind of thesame playbook as other types
(21:15):
of organizational change?
Deborah Ketai (21:17):
There are some differences.
First of all, anythingrelated to AI and ML is
going to move faster andfaster as time goes on.
The learning curveis just exponential.
I think that people whoare going to manage change
(21:38):
initiatives or projects orprograms in the AI space need to
get a sense of the foundationsof data process, infrastructure,
alignment with strategic goals,and for change management.
You always needbuy-in at the top.
(21:58):
You need buy-in from middlemanagement because they're gonna
provide the what's in it for me.
And you need mechanisms forthe ground level employees
to feed ideas back up andattitudes back up to the top.
There's also the questionof resistance to AI as a
(22:18):
concept, whether it's the.
Immediate reflex of,is my job secure?
And the answer to thatis maybe, maybe not.
Is anybody's job securein this day and age?
There's a lack of understanding.
There is often a lack oftransparency, both around the
(22:40):
use of AI within a company andsimply the lack of transparency
of the tools themselves andhow they're making decisions,
or if you're gonna allow themto make decisions, how they're
coming up with their output.
There's plenty of fear andrisks involved, and so I think
that there is a separate spacewithin change management for
(23:01):
people who are working on AIrelated change initiatives.
But I think the basictenets of change management
are the same, regardless.
Galen Low (23:12):
That makes sense to me in terms of, yeah, the
things you mentioned aboutbuy-in and transparency.
Yeah.
What really got me was the paceof change and that it's not just
like one change in organizationsthat I've worked in the past.
There is like a changemanagement team, right?
And they're gonna managethat one change and
then they walk away and.
Come back wheneverthey're needed, right?
For the next big change.
(23:33):
But what we're talking abouthere is actually continuous
change on sort of that like, Idon't wanna say shaky ground,
but you know, I think the thingyou said about job security and
fear of the technology and likethe black box decision making
within the technology, those areall things that sort of amplify
how people experience change.
(23:54):
Then it's exacerbated bythe fact that it doesn't
just end, you know?
I'm like to take the metaphortoo far, I'm picturing
like hunger Games, right?
You're like, okay, maybe ifthere's one, like one round
and you make it, you'relike, okay, I'm safe forever.
Whereas that's not the case.
It's gonna like kind of,it's continuous change.
I once got chided for sayingconstant change 'cause
constant, anyways, but thecontinuous change aspect
(24:16):
is a thing that makes it.
Complicated because it's notjust a straight line anymore,
like linear change management.
It's like, it's cyclical.
It's like a cycle of changethat almost needs to be managed
continuously because changeisn't, you know, hasn't stopped.
Deborah Ketai (24:31):
Right.
I mean, even withinthe AI, the individual
AI efforts themselves.
There's continual change.
Your data is driftingas time goes on.
So there are all kinds ofdifferent things that the
project manager or the changemanager or both need to take
into account that are differentbased on the science involved.
(24:52):
But as I said, that'skind of a small area
relative to the foundationsof project management
and change management.
Those are the same.
Galen Low (25:02):
Yeah, the frameworks are the same.
The stuff that goes insideof it, there will always be,
well, it couldn't make it tooeasy for ourselves, could we?
It's not gonna bethe same every time.
There's gonna be nuance, there'sgonna be complexities about
what type of change it is.
Deborah Ketai (25:16):
And one of the complexities that I think
people are just now starting torealize is that increasingly,
particularly with generativeAI, AI is itself a stakeholder.
It has its own.
You know, we're finding outthat it has its own goals,
its own attitudes, and itwill act differently and
(25:40):
sometimes contrary to theway you want it to based on
those internal realities.
Galen Low (25:46):
That's interesting.
I really like that.
We've been talking in thecommunity about almost like
adding your AI tools to thelike project communication
plan because it becomes asource of truth or you know,
a source of information.
So we do need tocommunicate updates to it.
You know, whether that'suploading meeting minutes
and stuff up to like thetools that we're using so
that they're up to date.
(26:06):
I like that whole notionof like, they've got
their own attitudes andperspectives on things.
And you know, this time twoyears ago, it would've been
like, that's sci-fi, but it'snot really, you know, we're
talking about agentic workflows.
We're, you know, we're talkingabout pursuing a general
artificial intelligence.
We're not there yet, but evenjust now, we do need to kind of
consider that because frankly,that's how it's built, right?
(26:29):
To either a, especially in thecase of generative AI, cater to
an individual user's preference,but maybe also behave
differently in front of anotheruser, which I guess is probably
the same with humans, but Ilike that idea that it needs
to be taken into account, thatit is a stakeholder, that it
itself is changing and it itselfneeds change management in a
(26:51):
way to understand the changethat are going on around it.
Deborah Ketai (26:54):
I think, you know, it's very much in my
mind, like belief in God.
I don't think you haveto believe in God.
I don't think you haveto believe in general
artificial intelligencein order to recognize.
That there are ways in whichthe world, or the ways in
(27:15):
which artificial intelligenceacts as though it had a
personality or as thoughthere were divine design.
And so you need to take thosethings into account and see how
you can limit guide, et cetera,or risk being overtaken by
(27:39):
events and situations that youdon't have any control over.
Galen Low (27:43):
I feel like we need to make some t-shirts that says
AI works in mysterious ways.
It's just not untrue.
You know, like all along hereI'm thinking of, you know,
you and your background,your role as sort of program
manager and change manager.
You are deeply involvedwith the project management
community and in my community.
(28:03):
There's some folks whoare being asked to kind
of do a dual role, right?
They're being askedto be project managers
and change managers.
In other words, it's hybridizingthis sort of PM and CM roles,
and it seems to be, at leastfrom where we stand, it seems
to be getting more common.
I wanted to get your takebecause you've kind of lived.
I guess first of all, likedo all projects need change
(28:25):
management and also do allproject managers need to
understand change management?
Deborah Ketai (28:30):
No, not all projects require
change management.
The orthodox view on thatis basically that a project
that is complex and that has.
A risk of not realizing itsbenefits if people fail to
adopt it once it's implemented.
And I'm not just talking abouttechnology projects there.
(28:54):
Those are the projects thatneed change management.
I don't think that it'sreasonable to expect project
managers to be change managers,but it is becoming the norm
in a lot of organizations.
The skill sets.
Are different but overlapping.
But what I tend to focus on isthe reason why you should have
(29:18):
separate roles is the timelines,because traditionally, project
managers are not given theopportunity to follow through
and really make sure thata change gets sustained.
Galen Low (29:33):
Interesting.
Yeah.
Deborah Keta (29:34):
That's really key.
So unless your PMO or operationsor whoever is governing your
project management processis willing to recognize
and fund the ability ofthe project managers to
ensure change sustainment.
(29:57):
I don't think it makes senseto have project managers
do change management.
But yes, they have tounderstand each other.
I think it's going to becomemore and more the norm and
more and more helpful to haveintegrated project and change
management plans and forproject managers who either
are forced to or are interestedin doing change management.
(30:20):
Some of the things that would bereally helpful to them to learn
some are our basic concepts.
To understand that successfulchange has to come from the
top, the bottom, and the middleof the corporate hierarchy.
To understand that, dependingon your organization, you may
need to be able to step intothe role of communications lead.
(30:41):
You may need to work withyour company's learning and
development function, oreven play that part yourself.
If your company doesn't haveone, you may need to establish
or re-engineer processes.
So things that are notnecessarily a strong focus
of most project managers.
They're there in projectmanagement, but they're
(31:03):
not really a focus tothe extent that they
are for change managers.
And the otherthing is that both.
Individual project managers andproject management organizations
need to be willing to andcommit to putting in a lot
of time and effort upfront onvarious kinds of assessments.
(31:24):
So stakeholder assessments,impact assessments,
readiness assessments.
Without those, the changeis not gonna be successful.
The adoption's not goingto be successful, or it's
going to be successful atfirst and then slip back.
Galen Low (31:38):
I like that sort of time aspect of things.
And even these sort of, youknow, up top, when you were
talking about your mission,your, you know, day to day it
almost was so zoomed out andfoundational and like almost.
It was in itself an initiative,which arguably is a project,
but also it probably set thefoundation to spawn other
projects, like for example.
Coordinating the hackathonwould be a project, right?
(32:00):
That you would have someoneideally come in and take the
lead on and deliver, you know,they could be in and out.
You did all this sort ofupfront foundation work
to sort of set up thechange and then afterwards.
You know, there's a periodof time longer than a project
where change is measured.
What I find interesting aboutit is that, you know, when
we're talking to the folksat the Project Management
(32:20):
Institute, one of the sortof big focuses now is driving
outcomes and doing more thanjust managing the iron triangle
and being more responsiblefor the value that we deliver.
And I think that comes up asone of the arguments as well
is like, but we're not alwaysaround to see that change
manifest and like, you know,are we setting ourselves up for
success if we're actually justmoving onto the next project
(32:44):
and actually have nothing todo with, you know, how that
impact is measured over time.
You know, are thosethings even compatible?
I don't even know if thisis a question in there.
Deborah Ketai (32:55):
Could it, I think there are a couple
of thoughts in there.
One that comes to mind isthat in the initial focus
on Agile, one of the coretenets was dedicated teams.
Well, that was one of thefirst things that slid Yes,
by the wayside when it wasactually implemented in
(33:16):
most large organizations.
And the fact is somebodyneeds to be dedicated to
following up on all this.
You can call thempart of the team.
You can call them a teamthat sits kind of above
the fray and communicates.
With the teams that come andgo, but somebody has to do that.
Galen Low (33:37):
I like that you framed it earlier
as an investment.
In other words, you know, aleadership team must be willing
to invest in having someonewho is that dedicated person
or team that oversees it.
And you know, I'm thinking aboutmy time in consulting or you
know, where we did have, it wasa whole separate division that
did sort of change management.
As a separate layer, and nowI'm starting to understand
(33:58):
why is because, you know,their aspect was different.
It started earlier and it endedlater in terms of like managing
change, measuring, you know,the impact of that change.
And then there's this sort ofthe middle bit right where the
project got done and deliveredand it would be a. Probably
non-viable investment for abusiness to say no, we'll just
keep that project team on tolike sit around and measure
(34:21):
their results for the next 18months and not do anything else.
Right.
And we need them tosort of shift along.
But I like that idea thatthe investment can be in a
dedicated person or a dedicatedgroup that oversees change
and that, you know, maybethe recommended direction
is not that project managersalso be change managers.
But that perhaps they couldswitch hit, like maybe you
(34:43):
could be the change managerrole in a certain initiative and
you could also be the projectmanager, maybe one at a time.
Not both at the same time,because yeah, it's hard
to be responsible formeasuring impact if you just
roll onto another projectafter a project goes live.
I guess maybe that kind of like.
It kind of triggers me tosort of zoom out a bit and
(35:06):
I'm thinking about, youknow, the role of a project
or a program manager.
From your perspective, how doyou think the role of a program
manager will evolve over thenext three to five years?
And maybe like, what skillsshould PMs be focusing
on today so that they canbe ready when it does?
Deborah Ketai (35:21):
For program managers and by program manager
I'm going to define it just forthe purposes of this question.
As somebody who manages a setof related projects and as a
set, the program may or may nothave defined start and end date.
(35:44):
It may be somethingthat's ongoing.
I think that program managersneed to get closer to where
strategy is formed and beable to influence strategy.
And to get senior leadership toclarify priorities, goals, and
(36:07):
to be transparent in reflectingback to leadership where there
are likely to be misalignments,where investments are not likely
to yield the appropriate return.
Where that return is likelyto be important in the
long run, but no guys, it'snot gonna give you 10 X
(36:30):
in the next fiscal year.
But it's still important.
I also think that programmanagers are going to need
to focus more on enterpriserisk and by risk I'm including
the classic, both negativerisks and opportunities.
Galen Low (36:45):
I love that you did that because
not a lot of people do.
Right?
The like opportunistic,like positive risks.
And honestly, I think thatmakes sense, especially
with that definition.
I'm glad you defined it upfrontbecause I'm kind of thinking
of that role where you're aprogram owner, your program
does not start and stop.
It's not just like a collectionof projects that you know
will end at a certaindate, but you actually
(37:06):
sort of own this program.
You have that perspective,right, to be looking beyond
a certain date to be, youknow, thinking of the business
strategy, I guess my questionwould be, have you found that
leadership teams want programmanagers at the strategic table?
Or are program managers who aretrying to be more strategic,
kind of like fighting toothand nail to get invited in?
(37:28):
And if so, what's thejourney look like there?
Deborah Ketai (37:30):
I'm going to use the classic
project manager response.
It depends.
It depends on theleader, it depends on the
organizational culture.
And I also think a lot oftimes we have to be proactive.
We can't just assume thatif we're not invited in
early, that it's becausethey don't want us there.
(37:53):
Sometimes it's justnobody's thought about it.
So over the course of thenext 3, 5, 10 years, building
relationships is going to becomemore and more an important
function of project and programmanagers, both because of.
AI impact on jobs in general.
(38:16):
And because they're going toneed to get closer and closer to
that source of power and vision.
And I think another thing thatis going to happen over the
next three to five years isthe most successful project and
program managers are going to bethe ones that kind of blur the
(38:37):
lines between business and it.
The ones that are coming froman IT background are really
going to have to learn whatmakes their business tick and
what makes their industry tick.
The ones that are coming froma business background are going
to have to learn somethingabout what enables the different
(38:58):
aspects of their business toshare data and information.
If it's healthcare to, you know,to process claims, to collect
money, they have to understandthe basics of all those
things, and so that line isgoing to blur as time goes on.
Galen Low (39:16):
I like that sort of almost how
pragmatic it is, right?
I think there's a lot ofLinkedIn posts and other sort
of media that's almost like youneed to flip the switch tomorrow
and become a strategic leaderand be at the table, you know?
Like now.
But I appreciate that,you know, your view.
It will take time, right?
You need to kind of be thatperson who's flagging risks
(39:37):
and showing that you understandthe business or showing that
you understand the technologyin your current role, and
then sort of stretch up andblend into that next role.
And I think that's areally, like a realistic
view of how this shiftis actually happening.
You know, everyone's like,well, it's gonna, AI is
gonna level everyone up, andthen we kind of stop there.
But none of us reallyknow what that means, but.
(39:59):
That's what it means.
It means, okay, like startbuilding these skills now.
Start buildingthese relationships.
Now, it's not that you aren'twanted at the table, it's just
that people don't know thatyou should be at the table yet.
That'll take time, and then yes,you will have this perspective.
Ideally the bandwidth tohave that perspective, to
look at it from a businessstrategy and tech strategy
(40:22):
lens, not just a projectscope, project budget lens.
Deborah Ketai (40:26):
Right and I mean, think of it in terms of your
own life as a project manager.
You already know that thereare other people who are not
invited in early enough, whetherit's the testing team, the
compliance and security folks.
We all have this problemof not being in early
enough in the conversation.
(40:47):
And if you need a wedgeinto that world, sometimes
it's helpful just to ask,to be invited to meetings
and promise you'll justbe a fly on the wall.
You are not going to engage,you're not, you know, whatever.
But at least be there and haveskip level meetings with your
(41:10):
managers so that you can startpercolating some of your ideas
up or some of your concerns up.
Galen Low (41:17):
I like that It almost brings us full circle
to what you're talking aboutat the beginning, which is
sort of getting the rightperspectives and talent in a
room together, or have themexchange knowledge and cross
train and sort of buildingcommunity because you know, if
we all are able to see it fromone another's perspectives.
Or even if we all start tosee things a little bit from
the same perspective, that'sa tie that lifts all boats.
(41:39):
You know, like that's whatcreates that cohesion.
That's what gives us theability to work together
cohesively, to address thechanges in front of us, address
the challenges in front ofus at speed and at scale.
Deborah Ketai (41:51):
I agree.
Galen Low (41:52):
Nice little bow.
Deborah, thanksso much for this.
Just for fun, do you have aquestion that you wanna ask me?
Deborah Ketai (41:59):
I do.
I don't know if you're going towanna answer it, but what is one
thing that you would love to dowith AI but are either afraid
to or don't know how to do?
Galen Low (42:07):
Oh my gosh.
Yeah.
The list is many, but I'm verysatisfied with the way a lot
of my generative AI tools workas like a thought partner,
and I haven't yet sort ofunleashed it to be autonomous
and not just agentic workflows,but like thinking of what I
would do if I had a projectcoordinator or an assistant
(42:28):
that's a human on my side tokind of take a ball and run
with it with limited direction.
And I know that's sort of whatpeople are trying to build,
but the way I'm seeing itright now is that a lot of
the agentic workflows havemore to do with intelligent
automation than they do.
Like sort of taking action.
It's almost like this blend,like I wish I could sort
(42:48):
of quote unquote prompt myassistant to go and do a few
things across different tools.
And I'm thinking of things likeagent mode in ChatGPT, and maybe
I need to play with it more.
But it is this sort of likecross tool administration that
is sort of by request, likeas it happens, not necessarily
sitting in the background asan AI agent doing its job.
(43:10):
I just crave a sort of moremultipurpose assistant who
is somewhat autonomous andsomewhat prompted like a
bit of that blend to kind ofgo out and just shave some
of that time off for me.
You know, I dunno if that's likea very vague answer, but I just
find myself sitting in betweenthe folks who are building
(43:32):
apps, you know, vibe, codingapps, folks who are building
agents that just work in thebackground and do stuff and
make decisions on their own.
And prompting, which is notquite the same for me as having
a. If I had a human assistantto go out and tackle some tasks
for me, that would be great.
I will prompt them.
They will make somedecisions themselves.
They'll come back.
You know, maybe that ishow people are building
(43:54):
agents right now, but Ijust haven't seen it yet.
Deborah Ketai (43:56):
Yeah.
What keeps me from doing it,frankly, is lack of trust in
where my data is going to go.
Galen Low (44:02):
Is that black box again.
Maybe this is a whole otherepisode, but you know, I think
that is one of the thingsthat gives everyone pause.
I was reading an articleand I can't remember where
it was for the life of me,but if I do remember, I'll
put it in the show notes.
But this notion of not adoptionslippage, I can't remember
the word, but it's just likepeople are kind of like hitting
a wall somewhere where they'relike, start using AI and then
(44:26):
at a certain point, you know,only a certain percentage is
using it for these types ofthings or at this frequency.
And I think one of the things,it's not necessarily just.
The technology or understandingit, but like the trust and
also the transparency arounddata governance and compliance
and where does this go andwhat am I allowed to do and
you know, can I connect?
(44:47):
Can I plug all these thingstogether and will it be safe?
Even just that thought, noteven to going through the
process, but even just thatthought creates hesitation.
But yeah, I relateto that completely.
That's me as well.
I'm like, what ifit does it wrong?
What if it's, you know,sending data everywhere?
This all kind ofhappens in a black box.
It could go sideways onme before it actually
creates a benefit.
Deborah Ketai (45:05):
I was recently at a PMI Regional Leadership
Conference, and one of thesession breakout sessions was
about AI and project management,and one of the things that
came up was the question, is itethical to have your chapters
(45:27):
email blasts facilitated by AI.
Galen Low (45:31):
Interesting.
Deborah Ketai (45:32):
And the outcome of the discussion
was that basically it wasnot ethical if it meant
releasing your members' data.
Galen Low (45:45):
Right.
Okay.
Deborah Ketai (45:45):
Into the void.
So unless you were using somekind of completely internal
tool, the answer was no.
Galen Low (45:52):
Isn't it interesting how it always kind of comes
back to guardrails that weourselves as humans, don't
fully understand, right?
They're discretionary decisionsusually, and then there's,
you know, policy and the sortof governance aspect, but
we don't yet trust ourselvesand our technology to kind
of build those guardrailsand put them in place so
that we have that trust.
(46:13):
Right.
That's very interesting.
I'd love to revisit that.
I'm always, I'm interestedin ideas and examples of how
AI is being used in projectmanagement and otherwise and
also what gives us pause,what obstacles we're facing.
Often it's not the technology,it's actually the ethics.
Deborah Ketai (46:30):
Yes.
Galen Low (46:31):
And the trust.
That's so interesting.
Deborah, thank you so much forspending the time with me today.
I really enjoyed myself.
For folks listening, where canpeople learn more about you?
Deborah Ketai (46:40):
Definitely connect with me on LinkedIn.
I'm the only DeborahKetai on LinkedIn.
And if you have theopportunity to add a note,
mention the DPM podcast.
Galen Low (46:51):
Awesome.
I will include a linkto your LinkedIn.
I'm happy that you'rethe only Deborah Ketai.
I'm like one of two GalenLows on LinkedIn, so
I have envy right now.
But I'll include that linkin the show notes for folks
to connect with Deborah.
Deborah, thank you again.
Deborah Ketai (47:04):
Thank you so much, Galen.
Galen Low (47:06):
That's it for today's episode of The Digital
Project Manager Podcast.
If you enjoyed thisconversation, make sure
to subscribe whereveryou're listening.
And if you want even moretactical insights, case studies
and playbooks, head over tothedigitalprojectmanager.com.
Until next time,thanks for listening.
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